Now, fill in the code to extract and call the savings column.

In order to calculate the mean, or the average by hand of the checkings columns, one can add each individual entry and divide by the total number or rows. This would take much time, but thankfully, R has a command for this.

We have done an example using the checkings column. Compute the same using the savings column.

Next, compute the standard deviation or spread of both the checkings and savings columns.

Now, to compute the SNR, the signal to noise ratio, a formula is created because there is no built in function.

SNR is the mean, or average, divided by the spread.

Of the Checking and Savings, which has a higher SNR? Why do you think that is?

Task 3

After using Watson Analytics to find patterns in the data, save your work and upload a screenshot here. Refer to Task 1 on how to upload a photo.

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